CDI Exerts Anti-Tumor Effects by Blocking the FoxM1-DNA Interaction.

The Forkhead box protein M1 (FoxM1) is an appealing target for anti-cancer therapeutics as this cell proliferation-associated transcription factor is overexpressed in most human cancers. FoxM1 is involved in tumor invasion, angiogenesis, and metastasis. To discover novel inhibitors that disrupt the FoxM1-DNA interaction, we identified CDI, a small molecule that inhibits the FoxM1-DNA interaction. CDI was identified through an assay based on the time-resolved fluorescence energy transfer response of a labeled consensus oligonucleotide that was bound to a recombinant FoxM1-dsDNA binding domain (FoxM1-DBD) protein and exhibited potent inhibitory activity against FoxM1-DNA interaction. CDI suppressed cell proliferation and induced apoptosis in MDA-MB-231 cells obtained from a breast cancer patient. Furthermore, it decreased not only the mRNA and protein expression of FoxM1 but also that of downstream targets such as CDC25b. Additionally, global transcript profiling of MDA-MB-231 cells by RNA-Seq showed that CDI decreases the expression of FoxM1-regulated genes. The docking and MD simulation results indicated that CDI likely binds to the DNA interaction site of FoxM1-DBD and inhibits the function of FoxM1-DBD. These results of CDI being a possible effective inhibitor of FoxM1-DNA interaction will encourage its usage in pharmaceutical applications.

Development of a FOXM1-DBD Binding Assay for High-Throughput Screening Using TR-FRET Assay.

Electrophoretic mobility shift assay (EMSA) technology has been widely employed for the analysis of transcription factors such as Forkhead box protein M1 (FOXM1). However, the application of high-throughput screening (HTS) in performing, such analyses are limited as it uses time consuming electrophoresis procedure and radioisotopes. In this study, we developed a FOXM1-DNA binding domain (DBD) binding assay based on time-resolved fluorescence energy transfer (TR-FRET) that enables HTS for the inhibitors of FOXM1-DNA interaction. This assay was robust, highly reproducible and could be easily miniaturized into 384-well plate format. The signal-to-background (S/B) ratio and Z' factor were calculated as 7.46 and 0.74, respectively, via a series of optimization of the assay conditions. A pilot library screening of 1019 natural compounds was performed using the FOXM1-DBD binding assay. Five hit compounds, namely, AC1LXM, BRN5, gangaleoidin, leoidin, and roemerine were identified as the inhibitors of FOXM1. In a cell viability assay, it was demonstrated that cell proliferation of FOXM1 overexpressed cell lines was suppressed in cell lines such as MDA-MB-231 and MCF-7 by five hit compounds. These results indicate that developed FOXM1-DBD binding assay can be applied to highly efficiency HTS of compound libraries.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach.

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.

Rapid identification of isoprenylated flavonoids constituents with inhibitory activity on bacterial neuraminidase from root barks of paper mulberry (Broussonetia papyrifera).

This study was to assess the possibility of using competitive and slow binding experiments with affinity-based ultrafiltration UPLC-QTof-MS analysis to identify potent bacterial neuraminidase (bNA) inhibitors from the Broussonetia papyrifera roots extract. To isolate unbound compounds from the enzyme-binding complex, the root bark extracts were either incubated in the absence of bNA, in the presence of bNA, or with the time-dependent bNA before the ultrafiltration was performed. Thirteen flavonoids were separated from the target extract, and their inhibitory activities were tested against bNA. The isolated flavonoids exhibited potent inhibition against NA (IC50 = 0.7-54.0 µM). Our kinetic analysis of representative active flavonoids (1, 2, and 6) showed slow and time-dependent reversible inhibition. Additionally, chalcones exhibited noncompetitive inhibition characteristics, whereas flavonols and flavans showed mixed-type behavior. The computational results supported the experimental behaviors of flavonoids 2, 6, 10, and 12, indicating that bounded to the active site, but flavonoids 6 and 10 binds near but not accurately at the active site. Although this is mixed-type inhibition, their binding can be considered competitive.

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.

Differential effects, on oncogenic pathway signalling, by derivatives of the HNF4 α inhibitor BI6015.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease with few "targeted" therapeutic options. Previously, we demonstrated involvement of the transcription factor HNF4α in human GC tumours, and the developmental signal mediator, WNT5A, as a prognostic GC biomarker. One previously developed HNF4α antagonist, BI6015, while not advancing beyond preclinical stages, remains useful for studying GC. METHODS: Here, we characterised the antineoplastic signalling activity of derivatives of BI6015, including transfer of the nitro group from the para position, relative to a methyl group on its benzene ring, to the ortho- and meta positions. We assessed binding efficacy, through surface plasmon resonance and docking studies, while biologic activity was assessed by antimitogenic efficacy against a panel of GC cell lines, and dysregulated transcriptomes, followed by pathway and subpathway analysis. RESULTS: The para derivative of BI6105 was found substantially more growth inhibitory, and effective, in downregulating numerous oncogenic signal pathways, including the embryonic cascade WNT. The ortho and meta derivatives, however, failed to downregulate WNT or other embryonic signalling pathways, unable to suppress GC growth. CONCLUSION: Straightforward strategies, employing bioinformatics analyses, to facilitate the effective design and development of "druggable" transcription factor inhibitors, are useful for targeting specific oncogenic signalling pathways, in GC and other cancers.

Development of Bacillus methanolicus methanol dehydrogenase with improved formaldehyde reduction activity.

Methanol dehydrogenase (MDH), an NAD+-dependent oxidoreductase, reversibly converts formaldehyde to methanol. This activity is a key step for both toxic formaldehyde elimination and methanol production in bacterial methylotrophy. We mutated decameric Bacillus methanolicus MDH by directed evolution and screened mutants for increased formaldehyde reduction activity in Escherichia coli. The mutant with the highest formaldehyde reduction activity had three amino acid substitutions: F213V, F289L, and F356S. To identify the individual contributions of these residues to the increased reduction activity, the activities of mutant variants were evaluated. F213V/F289L and F213V/F289L/F356S showed 25.3- and 52.8-fold higher catalytic efficiency (kcat/Km) than wild type MDH, respectively. In addition, they converted 5.9- and 6.4-fold more formaldehyde to methanol in vitro than the wild type enzyme. Computational modelling revealed that the three substituted residues were located at MDH oligomerization interfaces, and may influence oligomerization stability: F213V aids in dimer formation, and F289L and F356S in decamer formation. The substitutions may stabilise oligomerization, thereby increasing the formaldehyde reduction activity of MDH.